Tuesday, January 22, 2013

Excess Cancers and Deaths from Genetically Modified Feed

Reposted from Institute of Science in Society

That cancers are found even with a small number of rats tested is strong
evidence that the GM feed and herbicide are carcinogenic Prof Peter Saunders

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In September 2012, the research team led by Gilles-Eric Séralini at the
University of Caen published the findings of their feeding trial on rats to test
for toxicity of Monsanto’s genetically modified (GM) maize NK603 and/or Roundup
herbicide in the online edition of Food and Chemical Toxicology [1].

Séralini and his colleagues had previously found evidence for toxicity of GM
feed in data from Monsanto’s own experiments, which they had obtained through a
Freedom of Information demand [2]. Monsanto challenged their conclusions and, to
no one’s great surprise the European Food Standards Agency (EFSA) supported
Monsanto [3]. So the team decided to run their own experiment, using an
unusually large number of animals and over a period of about two years, roughly
the life expectancy of the rats, rather than the usual 90 days required in
toxicity trials including Monsanto’s.

What Séralini and his colleagues found was that NK603 and Roundup are not
only both toxic as expected, but also carcinogenic, which was unexpected. The
proportion of treated rats that died during the experiments was much greater
than the controls; moreover, in almost all groups a higher proportion developed
tumours, and the tumours appeared earlier.

As soon as the paper appeared, the GM lobby swung into action. In particular,
the Science Media Centre (SMC), a London-based organisation partly funded by
industry, quickly obtained quotes from a number of pro-GM scientists and
distributed them to the media [4]. According to a report in Times Higher
Education [5], the SMC succeeded in influencing the coverage of the story in
the UK press and largely kept it off the television news.

Séralini has rebutted the pro-GM critics point by point on the CRIIGEN
website [6]. The statistician Paul Deheuvels, a professor at the Université
Pierre et Marie Curie in Paris and a member of the French Académie des sciences,
has now drawn attention to another serious error in the criticisms [7]: the
complaint that Séralini used only 10 rats per group when the OECD guidelines [8]
recommend 50 for investigations on carcinogenesis. Because the experiments did
not follow the accepted protocol, their results, they argue, can be safely
ignored.

In the first place, this was not a wilful disregard of the guidelines. The
experiment was designed to test for toxicity, and for that the recommended group
size is 10.

But Deheuvels pointed out that the fact Séralini and his colleagues had used
smaller groups than recommended makes the results if anything more
convincing, not less. That is because using a smaller number of rats actually
made it less likely to observe any effect. The fact that an effect was
observed despite the small number of animals made the result all the more
serious.

To see why, we have to look carefully at how common statistical tests are
carried out. We begin with a null hypothesis, which as the name suggests is
essentially the hypothesis that nothing unusual has happened. Here it is the
hypothesis that rats fed on GMOs and/or herbicide are no more likely to develop
cancer than the controls. Clearly, we would like to reject the null hypothesis
if it is false and accept it if it is true. But statistics is about taking
decisions in the face of uncertainty – if there were no uncertainty there would
be no need to use statistics – and so however careful we are, we may come to the
wrong conclusion.

There are two ways in which we can go wrong. On the one hand, we can make a
“Type 1 error” in rejecting the null hypothesis when it is correct. Here that
would mean reporting that GMO and/or herbicide are carcinogenic when they are
not. Or, we can make a “Type 2 error” in accepting the null hypothesis when it
is false. Here that would mean reporting that GMO and/or herbicide are not
carcinogenic when in fact they are.

Naturally we would like to design experiments to make either of those
probabilities as small as possible, but there is a problem. The two types of
error are linked. We can reduce the probability of making a Type 1 error by
requiring stronger evidence before we reject the null hypothesis. But if we do
that we necessarily require less evidence to accept it, but that increases the
probability of making a Type 2 error. We have to find a balance, and usually
what we do is insist that the probability of a Type 1 error must be very small,
conventionally 0.05. That’s the origin of the “significant at 5 percent” level.

A probability of 0.05 is very small, so what we are saying is that we will
only accept that the effect is real if we can be convinced “beyond reasonable
doubt”; and most of the time that makes sense. If you’re thinking of installing
a new manufacturing process or a new way of running your farm, you want to be
very confident that it really is better before you make a major investment.

It is not so obviously sensible when safety is concerned. If there is
scientific evidence that a product is hazardous, then it is hardly surprising if
the manufacturer would not want to withdraw it unless the evidence is very
strong indeed. The rest of us, however, might take a different view. Are we
really willing to accept NK603 maize, or Roundup herbicide, unless and until
they have been shown beyond reasonable doubt to be carcinogenic?

The standard statistical test does seem to be the wrong way around, but
that’s partly because so far we have only been considering the Type 1 error, the
false positive. But as Deheuvels reminds us, there is also the Type 2 error, the
false negative. If NK603 and/or the herbicide are actually carcinogenic, what is
the probability that we will fail to observe that?

The way to reduce the probability of a Type 2 error is to use larger groups.
Because we would expect carcinogenicity to be slower to appear and harder to
detect than toxicity, the group size for experiments on carcinogenicity
should be larger than for toxicity, and this is precisely what the OECD
Guidelines require.

If the experiment had not detected carcinogenicity, that might have
been because the groups were too small. As the experiment did detect it, that
the groups were small is not an issue. The scientists who were asked to supply
sound bites for the Science Media Centre were quick to object that Séralini and
his group had used the protocol for testing toxicity rather than the one for
carcinogenesis. Had they taken a moment to ask themselves why the two protocols
are different, they would have realised that in using the toxicity protocol (and
remember, that was because it was what the experiment was designed to test)
Séralini and his group made it less likely that they would detect
carcinogenesis. To criticise a result because the experiment was conducted in a
way that was more conservative than required is totally
unjustifiable.